• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Tan Qingfeng, Shi Jinqiao, Fang Binxing, Guo Li, Zhang Wentao, Wang Xuebin, Wei Bingjie. Towards Measuring Unobservability in Anonymous Communication Systems[J]. Journal of Computer Research and Development, 2015, 52(10): 2373-2381. DOI: 10.7544/issn1000-1239.2015.20150562
Citation: Tan Qingfeng, Shi Jinqiao, Fang Binxing, Guo Li, Zhang Wentao, Wang Xuebin, Wei Bingjie. Towards Measuring Unobservability in Anonymous Communication Systems[J]. Journal of Computer Research and Development, 2015, 52(10): 2373-2381. DOI: 10.7544/issn1000-1239.2015.20150562

Towards Measuring Unobservability in Anonymous Communication Systems

More Information
  • Published Date: September 30, 2015
  • Anonymous communication technique is one of the main privacy-preserving techniques, which has been widely used to protect Internet users’ privacy. However, existing anonymous communication systems are particularly vulnerable to traffic analysis, and researchers have been improving unobservability of systems against Internet censorship and surveillance. However, how to quantify the degree of unobservability is a key challenge in anonymous communication systems. We model anonymous communication systems as an alternating turing machine, and analyze adversaries’ threat model. Based on this model, this paper proposes a relative entropy approach that allows to quantify the degree of unobservability for anonymous communication systems. The degree of unobservability is based on the probabilities of the observed flow patterns by attackers. We also apply this approach to measure the pluggable transports of TOR, and show how to calculate it for comparing the level of unobservability of these systems. The experimental results show that it is useful to evaluate the level of unobservability of anonymous communication systems. Finally, we present the conclusion and discuss future work on measuring unobservability in anonymous communication systems.
  • Related Articles

    [1]Attention-enhanced Semantic Fusion Knowledge Graph Representation Learning Framework[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202440669
    [2]He Peng, Zhou Gang, Chen Jing, Zhang Mengli, Ning Yuanlong. Type-Enhanced Temporal Knowledge Graph Representation Learning Model[J]. Journal of Computer Research and Development, 2023, 60(4): 916-929. DOI: 10.7544/issn1000-1239.202111246
    [3]Hu Xuyang, Wang Zhizheng, Sun Yuanyuan, Xu Bo, Lin Hongfei. Knowledge Graph Representation Method Combined with Semantic Parsing[J]. Journal of Computer Research and Development, 2022, 59(12): 2878-2888. DOI: 10.7544/issn1000-1239.20210849
    [4]Ning Yuanlong, Zhou Gang, Lu Jicang, Yang Dawei, Zhang Tian. A Representation Learning Method of Knowledge Graph Integrating Relation Path and Entity Description Information[J]. Journal of Computer Research and Development, 2022, 59(9): 1966-1979. DOI: 10.7544/issn1000-1239.20210651
    [5]Ma Ang, Yu Yanhua, Yang Shengli, Shi Chuan, Li Jie, Cai Xiuxiu. Survey of Knowledge Graph Based on Reinforcement Learning[J]. Journal of Computer Research and Development, 2022, 59(8): 1694-1722. DOI: 10.7544/issn1000-1239.20211264
    [6]Zhu Hongrui, Yuan Guojun, Yao Chengji, Tan Guangming, Wang Zhan, Hu Zhongzhe, Zhang Xiaoyang, An Xuejun. Survey on Network of Distributed Deep Learning Training[J]. Journal of Computer Research and Development, 2021, 58(1): 98-115. DOI: 10.7544/issn1000-1239.2021.20190881
    [7]Yao Siyu, Zhao Tianzhe, Wang Ruijie, Liu Jun. Rule-Guided Joint Embedding Learning of Knowledge Graphs[J]. Journal of Computer Research and Development, 2020, 57(12): 2514-2522. DOI: 10.7544/issn1000-1239.2020.20200741
    [8]Du Zhijuan, Du Zhirong, Wang Lu. Open Knowledge Graph Representation Learning Based on Neighbors and Semantic Affinity[J]. Journal of Computer Research and Development, 2019, 56(12): 2549-2561. DOI: 10.7544/issn1000-1239.2019.20190648
    [9]Yang Xiaohui, Wan Rui, Zhang Haibin, Zeng Yifu, Liu Qiao. Semantical Symbol Mapping Embedding Learning Algorithm for Knowledge Graph[J]. Journal of Computer Research and Development, 2018, 55(8): 1773-1784. DOI: 10.7544/issn1000-1239.2018.20180248
    [10]Fang Yang, Zhao Xiang, Tan Zhen, Yang Shiyu, Xiao Weidong. A Revised Translation-Based Method for Knowledge Graph Representation[J]. Journal of Computer Research and Development, 2018, 55(1): 139-150. DOI: 10.7544/issn1000-1239.2018.20160723

Catalog

    Article views (11511) PDF downloads (4604) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return